How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center
Abstract Objective To develop a predictive tool in the form of a Nomogram based on the Cox regression model, which incorporates the impact of the length of treatment cycles on the outcome of live birth, to evaluate the probability of infertile couples having a live birth after one or more complete c...
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BMC
2025-01-01
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| Series: | BMC Pregnancy and Childbirth |
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| Online Access: | https://doi.org/10.1186/s12884-024-07017-6 |
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| author | Xiangyi Kong Zhiqiang Liu Chunyu Huang Xiuyu Hu Meilan Mo Hongzhan Zhang Yong Zeng |
| author_facet | Xiangyi Kong Zhiqiang Liu Chunyu Huang Xiuyu Hu Meilan Mo Hongzhan Zhang Yong Zeng |
| author_sort | Xiangyi Kong |
| collection | DOAJ |
| description | Abstract Objective To develop a predictive tool in the form of a Nomogram based on the Cox regression model, which incorporates the impact of the length of treatment cycles on the outcome of live birth, to evaluate the probability of infertile couples having a live birth after one or more complete cycles of In Vitro Fertilization (IVF), and to provide patients with a risk assessment that is easy to understand and visualize. Methods A retrospective study for establishing a prediction model was conducted in the reproductive center of Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital). A total of 4413 patients who completed ovarian stimulation treatment and reached the trigger were involved. 70% of the patients were randomly placed into the training set (n = 3089) and the remaining 30% of the patients were placed into the validation set (n = 1324) randomly. Live birth rate (LBR) and cumulative LBR (CLBR) were calculated for one retrieval cycle and the subsequent five frozen embryo transfer (FET) cycles. Proportional Hazards (PH) Assumption test was used for selecting the parameter in the predictive model. A Cox regression model was built based on the basis of training set, and ROC curves were used to test the specificity and sensitivity of the prediction model. Subsequently, the validation set was applied to verify the validity of the model. Finally, for a more intuitive assessment of the CLBR more intuitively for clinicians and patients, a Nomogram model was established based on predictive model. By calculating the scores of the model, the clinicians could more effectively predict the probability for an individual patient to obtain at least one live birth. Results In the fresh embryo transfer cycle, the LBR was 38.7%. In the first to fifth FET cycle, the optimal estimate and conservative estimate CLBRs were 59.95%, 65.41%, 66.35%, 66.58%, 66.61% and 56.81%, 60.84%, 61.50%, 61.66%, 61.68%, respectively. Based on PH test results, the potential predictive factors for live birth were insemination method, infertility factors, serum progesterone level (R = 0.043, p = 0.059), and luteinizing hormone level (R = 0.015, p = 0.499) on the day initiated with gonadotropin, basal follicle-stimulating hormone (R = -0.042, p = 0.069) and BMI (R = -0.035, p = 0.123). We used ROC curve to test the predictive power of the model. The AUC was 0.782 (p < 0.01, 95% CI: 0.764–0.801). Then the model was verified using the validation data. The AUC was 0.801 (p < 0.01, 95% CI: 0.774–0.828). A Nomogram model was built based on potential predictive factors that might influence the event of a live birth. Conclusions The Cox regression and Nomogram prediction models effectively predicted the probability of infertile couples having a live birth. Therefore, this model could assist clinicians with making clinical decisions and providing guidance for patients. Trial registration N/A. |
| format | Article |
| id | doaj-art-43947a1b34864e80b51f70cb06a41705 |
| institution | Kabale University |
| issn | 1471-2393 |
| language | English |
| publishDate | 2025-01-01 |
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| series | BMC Pregnancy and Childbirth |
| spelling | doaj-art-43947a1b34864e80b51f70cb06a417052025-02-02T12:46:53ZengBMCBMC Pregnancy and Childbirth1471-23932025-01-0125111310.1186/s12884-024-07017-6How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-centerXiangyi Kong0Zhiqiang Liu1Chunyu Huang2Xiuyu Hu3Meilan Mo4Hongzhan Zhang5Yong Zeng6Reproductive Center of Shenzhen Zhongshan Obstetrics and Gynecology Hospital Formerly Reproductive Center of Shenzhen Zhongshan Urology HospitalReproductive Center of Shenzhen Zhongshan Obstetrics and Gynecology Hospital Formerly Reproductive Center of Shenzhen Zhongshan Urology HospitalReproductive Center of Shenzhen Zhongshan Obstetrics and Gynecology Hospital Formerly Reproductive Center of Shenzhen Zhongshan Urology HospitalReproductive Center of Shenzhen Zhongshan Obstetrics and Gynecology Hospital Formerly Reproductive Center of Shenzhen Zhongshan Urology HospitalReproductive Center of Shenzhen Zhongshan Obstetrics and Gynecology Hospital Formerly Reproductive Center of Shenzhen Zhongshan Urology HospitalReproductive Center of Shenzhen Zhongshan Obstetrics and Gynecology Hospital Formerly Reproductive Center of Shenzhen Zhongshan Urology HospitalReproductive Center of Shenzhen Zhongshan Obstetrics and Gynecology Hospital Formerly Reproductive Center of Shenzhen Zhongshan Urology HospitalAbstract Objective To develop a predictive tool in the form of a Nomogram based on the Cox regression model, which incorporates the impact of the length of treatment cycles on the outcome of live birth, to evaluate the probability of infertile couples having a live birth after one or more complete cycles of In Vitro Fertilization (IVF), and to provide patients with a risk assessment that is easy to understand and visualize. Methods A retrospective study for establishing a prediction model was conducted in the reproductive center of Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital). A total of 4413 patients who completed ovarian stimulation treatment and reached the trigger were involved. 70% of the patients were randomly placed into the training set (n = 3089) and the remaining 30% of the patients were placed into the validation set (n = 1324) randomly. Live birth rate (LBR) and cumulative LBR (CLBR) were calculated for one retrieval cycle and the subsequent five frozen embryo transfer (FET) cycles. Proportional Hazards (PH) Assumption test was used for selecting the parameter in the predictive model. A Cox regression model was built based on the basis of training set, and ROC curves were used to test the specificity and sensitivity of the prediction model. Subsequently, the validation set was applied to verify the validity of the model. Finally, for a more intuitive assessment of the CLBR more intuitively for clinicians and patients, a Nomogram model was established based on predictive model. By calculating the scores of the model, the clinicians could more effectively predict the probability for an individual patient to obtain at least one live birth. Results In the fresh embryo transfer cycle, the LBR was 38.7%. In the first to fifth FET cycle, the optimal estimate and conservative estimate CLBRs were 59.95%, 65.41%, 66.35%, 66.58%, 66.61% and 56.81%, 60.84%, 61.50%, 61.66%, 61.68%, respectively. Based on PH test results, the potential predictive factors for live birth were insemination method, infertility factors, serum progesterone level (R = 0.043, p = 0.059), and luteinizing hormone level (R = 0.015, p = 0.499) on the day initiated with gonadotropin, basal follicle-stimulating hormone (R = -0.042, p = 0.069) and BMI (R = -0.035, p = 0.123). We used ROC curve to test the predictive power of the model. The AUC was 0.782 (p < 0.01, 95% CI: 0.764–0.801). Then the model was verified using the validation data. The AUC was 0.801 (p < 0.01, 95% CI: 0.774–0.828). A Nomogram model was built based on potential predictive factors that might influence the event of a live birth. Conclusions The Cox regression and Nomogram prediction models effectively predicted the probability of infertile couples having a live birth. Therefore, this model could assist clinicians with making clinical decisions and providing guidance for patients. Trial registration N/A.https://doi.org/10.1186/s12884-024-07017-6In vitro fertilizationCumulative live birth rateCox regression modelNomogram modelPredictive factors |
| spellingShingle | Xiangyi Kong Zhiqiang Liu Chunyu Huang Xiuyu Hu Meilan Mo Hongzhan Zhang Yong Zeng How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center BMC Pregnancy and Childbirth In vitro fertilization Cumulative live birth rate Cox regression model Nomogram model Predictive factors |
| title | How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center |
| title_full | How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center |
| title_fullStr | How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center |
| title_full_unstemmed | How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center |
| title_short | How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center |
| title_sort | how to estimate the probability of a live birth after one or more complete ivf cycles the development of a novel model in a single center |
| topic | In vitro fertilization Cumulative live birth rate Cox regression model Nomogram model Predictive factors |
| url | https://doi.org/10.1186/s12884-024-07017-6 |
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